A unified race algorithm for offline parameter tuning

This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods

A concise guide to existing and emerging vehicle routing problem variants

Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges

Efficient Inventory Management of Hospital Supply Chains Using a Sim-Heuristic Approach

Inventory management is a vital section of a supply chain system. In a hospital setting, where delivering high quality patient care is a prime concern, inventory management is often overlooked. With the ever increasing demand for products, it becomes challenging to manage inventory in a dynamic facility such as a hospital. Although there is abundant research in supply chain, seldom have the proposed methods found their way into execution in actual hospital settings. Additionally, much of the literature focuses on particular aspects of the supply chain. Current methods used in practice lead to system performance that is suboptimal, resulting in too much or too short inventory in stock, overtime work to manage supplies, expedited shipments and potentially substandard quality of care delivered to patients. Having the right products available at the point-of-use is important to the efficient and effective treatment of patients. With cost and budget constraints, merely managing demand is not sufficient. There is a need to develop a system design which enables hospitals and healthcare institutions to implement and benefit from methods that have been developed or are being developed for optimal inventory management systems. In this research, we study the hospital supply chain from manufacturers/distribution centers to the point-of-use within a hospital unit, taking into account the integration and implementation of the various echelon of the supply chain system. In particular, we design and develop a sim-heuristic methodology using operations research to evaluate inventory and operational decision variables based on service level and operational costs, subject to variability in demand and lead-time. In addition, we demonstrate the capabilities and limitations of the methodology and compare alternate system configurations including a (Q, r) inventory system and Kanban system

Developing sustainable supply chains in regional Australia considering demand uncertainty, government subsidies and carbon tax regulation

There is a tremendous opportunity to implement sustainable supply chain management practices in terms of logistics, operations, and transport network in regional Australia. Unfortunately, this opportunity has not been investigated and there is a lack of academic studies in this body of knowledge. This thesis is made up by three related, but independent models designed to efficiently distribute products from a regional hub to other part of the country. This research aims to develop efficient and sustainable supply chain practices to deliver regional Australian products across the country and overseas. As the airports of most Australian capital cities are over-crowded while many regional airports are under-utilised, the first model examines the ways to promote the use of regional airports. Australia is a significant food producer and the agricultural products are primarily produced in regional areas. In the other two models, we focus on the distribution of perishable products from regional Australia. The first model presented in Chapter 2 outlines how different government subsidy schemes can be used to influence airfreight distributions that favour the use of regional airports and promote regional economic development. The model simultaneously considers time-window and release-time constraints as well as the heterogeneous fleet for ground distribution where fuel consumption is subject to load, travel distance, speed and vehicle characteristics. A real-world case study in the state of Queensland, Australia is used to demonstrate the application of the model. The results suggest that the regional airport's advantages can be promoted with suitable subsidy programs and the logistics costs can be reduced by using the regional airport from the industry’s perspective. The second model presented in Chapter 3 examines the impacts of carbon emissions arising from the storage and transportation of perishable products on logistical decisions in the cold supply chain considering carbon tax regulation and uncertain demand. The problem is formulated as a two-stage stochastic programming model where Monte Carlo approach is used to generate scenarios. The aim of the model is to determine optimal replenishment policies and transportation schedules to minimise both operational and emissions costs. A matheuristic algorithm based on the Iterated Local Search (ILS) algorithm and a mixed integer programming is developed to solve the problem in realistic sizes. The proposed model was implemented in a real-world case study in the state of Queensland, Australia to demonstrate the application of the model. The results highlight that a higher emissions price does not always contribute to the efficiency of the cold supply chain system. The third model presented in Chapter 4 investigates the impacts of two different transport modes - road and rail - on the efficiency and sustainability of transport network to deliver meat and livestock from regional Queensland to large cities and seaports. The model is formulated as a mixed-integer linear programming model that considers road traffic congestions, animal welfare, quality of meat products and environmental impacts from fuel consumption of different transport modes. The aim of the model is to determine an optimal network configuration where each leg of journey is conducted by the most reliable, sustainable and efficient transport mode. The results indicate that it would be possible to significantly decrease total cost if a road-rail intermodal network is used. Considering animal welfare, product quality and traffic congestion can have a significant effect on the decisions related to transport mode selection

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development